1
Master Thesis
submitted within the UNIGIS MSc. programme at the Centre for Geoinformatics (Z_GIS)
University of Salzburg, Austria
under the provisions of UNIGIS joint study programme with Goa University, India
GEOGRAPHIC INFORMATION SYSTEM BASED SPATIAL DECISION MAKKING STUDIES AT
ANNAMALAI NAGAR AREA, SOUTH INDIA
by
Gurugnanam-Balasubramaniyan_2008_Project Report
A thesis submitted in partial fulfilment of the requirements of the degree of
Master of Science (Geographical Information Science & Systems) – MSc (GISc) Advisor (s):
Dr. Shanavaz
India, 02.05.2010
INDEX
CHAPTER CONTENT Page No.
Science Pledge and Acknowledgement 3
Content 4
List of Tables 7
List of Figures 8
Abstract 10
I
Introduction 11
II
Thematic Map Generation 14
III
Processes and Results 27
IV
Research Summary 58
Bibliography 61
3
Science Pledge
By my signature below, I certify that my project report is entirely the result of my own work. I have cited all sources of information and data I have used in my project report and indicated their origin.
India 02.05.2010 s/d Gurugnanam Balasubramaniyan
Place and Date Signature
Acknowledgements:
I feel extremely delighted to place on record my sincere thanks to Dr.Shanawaz, for his guidance and valuable inputs and constant encouragement throughout my research work.
Sl.
No. CONTENT Page
No.
CHAPTER I INTRODUCTION
1.1 Introduction 11
1.2 Aim 12
1.3 Research Objectives 12
1.4 Study Area 12
1.6 Organization of the Document 12
CHAPTER II
THEMATIC MAP GENERATION 14
2.1 Study area map Generation 14
2.1.1 Data
14
2.1.2 Data Processing
14
2.2
Road Network Map Preparation15
2.2.1 Thematic Map – Roads
15
2.2.2 Road Traffic Map Preparation
15
2.2.3 Road Widen (Buffer) – Road and Land use / land cover
Overlay Map Preparation
17
2.2.4 Road Network Analysis
17
2.2.5 Service area Analysis (Network Analysis)
19
2.3
Waterbody Map Preparations20
2.4
River21
2.4.1 River Map Preparation
21
2.4.2 Flood Hazard Zone Map preparation
21
2.5
Remote Sensing21
2.5.1 Image Enhancement 21
5
2.5.2 Land use / Land cover map preparation from Satellite
Image 24
2.5.3 Land use / Land cover map preparation from Google
Image 24
2.6 Overlay Analysis output Thematic Map preparation 24 2.6.1 Road over Land use/ Land cover map preparation 24 2.6.2 River buffered zone map over village map – Flood zone
hazard map preparation 25
2.7 Change Detection Analysis Map Preparation 25 2.8 GIS Based Solutions for Waste Disposals 26
CHAPTER III
PROCESSES AND RESULTS 27
3.1 Introduction 27
3.2 Road Network Analysis – Change Detection Analysis 27
3.3 Road Traffic Analysis 28
3.4 Road Network Analysis – Shortest Route Assessment 33 3.5 Service Area Analysis (Network Analysis) 35 3.6 Waterbodies - Change Detection Analysis 35
3.7 River – Change Detection Analysis 36
3.8 Data and Maps Analysis for Change Detection 37
3.9 2004 – 2009 Period Error Matrix 39
3.10 Floods Affected Area Demarcation – Buffered Analysis 40 3.11 Image Enhancements Study – Vegetation Class Analysis 42
3.11.1 Low-Pass Filtering 42
3.11.2 High-Pass Filtering 43
3.12 Spatial Queries 43
3.13 GIS Based Solutions for Waste Disposals 50
3.13.1 Introduction 50
3.13.2 Geology 51
3.13.3 Soil Depth 52
3.13.4 Soil Permeability 52
3.13.5 The New Concepts 53
3.13.6 GIS Analysis 53
CHAPTER IV
RESEARCH SUMMARY
58
4.1 Achievements of the Research 58
4.2 Conclusion 59
4.2.1 Change Detection Studies 59
4.2.2 Road Network Analysis 59
4.2.3 GIS Overlay Analysis 59
4.2.4 GIS – Waste Disposal Site Location Assessment 60
5 Bibliography 61
7
LIST OF TABLES Table
No. TITLE OF THE TABLES Page
No.
2.1 Feature Class and Feature Type 15
2.2 Land use/ Land cover classification (NRSA) 23 3.1 Change Detection Analysis Results of Roads – Toposheet, Satellite
Image and Google Image 29
3.2 Road Traffic Result – In Annamalai Nagar 31 3.3 Road 10m Buffered Zone Results – Annamalai Nagar 33 3.4 Road 10m Buffered map and land use/Land cover map Overlay
Result – in Annamalai Nagar 33
3.5 GIS Output – Waterbody Results and its Changes 36 3.6 GIS Output – River Length Results and its Changes 36 3.7 GIS Output – Land Use/Land Cover Distribution Results 38 3.8 Error Matrix of the IRS P6 LISS-IV MX Data 2004 Vs Google Image
2009 39
3.9 River 500m Buffered Result – in Annamalai Nagar and in
Surrounding area 41
3.10 Village Wise Flood Area – in Annamalai Nagar and in Surrounding
area 41
3.11 Geology – GIS Spatial Distribution Results 51 3.12 Soil Depth – GIS Spatial Distribution Results 52 3.13 Soil Permeability – GIS Spatial Distribution Results 53 3.14 Various Data Layers and Waste Disposals Weighting–Rating System
Adopted in this Study 54
3.15 Geology with Soil Depth integrated map – GIS Spatial Distribution
Results 56
3.16 Site Section for Waste Disposal Area – GIS Spatial Distribution
Results 57
LIST OF FIGURES Figure
No. TITLE OF THE FIGURE Page
No.
1.1 Study Area Map (Annamalai University Satellite image map) 13
1.2 Study Area Map (Google image map) 13
2.1 Road Network Map – Derived from Toposheet – Annamalai Nagar 16 2.2 Road Network Map – Derived from IRS P6 Satellite Image – Annamalai Nagar 16 2.3 Road Network Map – Derived from Google Image – Annamalai Nagar 16 2.4 Road Network Traffic Map – Derived from Google Image – Annamalai Nagar 16 2.5 Road 10m Buffered Map – Derived from Google Image – Annamalai Nagar 18 2.6 Road Network Analysis Methodology Flow chart 17 2.7 Waterbody Map – Derived from Toposheet – Annamalai Nagar 18 2.8 Waterbody Map – Derived from IRS P6 Satellite image – Annamalai Nagar 20 2.9 Waterbody Map – Derived from Google Image – Annamalai Nagar 20 2.10 River Map – Derived from Toposheet – Annamalai Nagar 22 2.11 River Map – Derived from IRS P6 Satellite Image– Annamalai Nagar 22 2.12 River Map – Derived from Google Image– Annamalai Nagar 22 2.13 Land Use/ land Cover Map – Derived from IRS P6 Satellite Image – Annamalai
Nagar 22
2.14 Land Use/ land Cover Map – Derived from Google Image – Annamalai Nagar 25 2.15 Flow Chart of Change Detection Studies Methodology 26 3.1 Toposheet, Satellite Image and Google Image Road – Overlay map 28 3.2 Google Image Road – Highly Traffic Road – Two Side 10m Buffered Zone Map 28 3.3 Road 10m Buffered Zone Map and land Use/land Cover Map Overlay Map 29 3.4 From Karur vysya bank To Tamil Department (Annamalai University) Road
Stops Created Map 34
3.5 Alternate Shortest Route Map 34
3.6 Service Area from the Centre Point 35
3.7 Satellite Image – Land Use/Land Cover Map 37
9
3.8 Google Image – Land Use/Land Cover Map 37
3.9 Land Use/Land Cover – IRS P6 Satellite Image and Google Image Overlay Map 38 3.10 Change Detection Studies Using IRS P6 LISS-IV MX and Google Image 38
3.11 River – 500m Buffered Zone map 42
3.12 Village Wise Flood Affected Area Map 42
3.13 Low pass Filtering Output Map 45
3.14 High pass Filtering Output Map 45
3.15 Spatial Query for the Decision Makers Through Definition Query for Faculty of
Arts – Annamalai University 46
3.16 Spatial Query for the Decision Makers Through Definition Query for Faculty of
Science – Annamalai University 46
3.17 Spatial Query for the Decision Makers Through Definition Query for Faculty of
Engineering – Annamalai University 47
3.18 Spatial Query for the Decision Makers Through Definition Query for Dept. of
Physical Science – Annamalai University 47
3.19 Spatial Query for the Decision Makers Through Definition Query for
Administrative Office – Annamalai University 48
3.20 Spatial Query for the Decision Makers Through Definition Query for Faculty of
Medical Science – Annamalai University 48
3.21 Spatial Query for the Decision Makers Through Definition Query for University
OP Hospital – Annamalai University 49
3.22 Spatial Query for the Decision Makers Through Definition Query for University
Old OP Hospital – Annamalai University 49
3.23 Spatial Query for the Decision Makers Through Definition Query for Ayyappan
Temple – Annamalai University 50
3.24 Spatial Query for the Decision Makers Through Definition Query for Boys Hostel
– Annamalai University 50
3.25 Geology Map 55
3.26 Soil Depth Map 55
3.27 Soil Permeability Map 56
3.28 Geology and Soil Depth Integration Map 57
3.29 Waste Disposals Area Map 57
Abstract:
The present work is aimed in spatial analysis for the spatial decision makers. The annamalai nagar, South India is taken up for the present study. The spatial development in this area is very high. This makes to study the following problems. i.e. Road Development, Water Scarcity during summer, flooding during monsoon. This is due to the high student’s population in the recent decades in Annamalai University. To solve the said spatial problems, GIS was used to find out the solution for the spatial planners. The multi date data sets were used. Toposheet (1971), IRS P6 LIV Satellite image (2004), GeoEye image (2010) data was used for the preparation of thematic maps. Except Satellite data, the other data were scanned and Registered in ArcGIS environment. Thematic maps on Road, Waterbody, River maps were individually digitized and overlaid one over the other to find the change detection analysis. Road traffic analyses were carried out for the spatial decision makers.
In road network analysis, finding the shortest route between the destinations and service area is also assessed for the spatial decision makers. The land use/land cover maps were prepared through the satellite data and Google image. The changes were brought out in the present study. This will be highly useful for the decision makers to take any spatial related planning. Overlay analysis were used to find out the land for the future planning.
Flood hazard zonation mapping in the study were carried at village level. Overall, the study reveals that the use and application capacity of GIS.
11
CHAPTER I INTRODUCTION
1.1 Introduction
In recent period, study of land use has become very important factor for global change, as land use change has direct impacts on environment. Transformation of land use always requires high attention and has higher priority. Since the last decade, more attention has been paid to land use change because human activities are affecting the ecosystems as population is growing very rapidly. The land use change can be mapped and monitored using satellite data (Joshi and Suthar, 2002; Ayad, 2005; Bothale and Sharma, 2007; Kent and Gullari, 2007; Taubenbock, et al., 2008) systematically and efficiently. Mapping and monitoring of land use includes information on change pattern, dimension and transformation in between the different categories of land. Remote sensing is a good tool for identifying threats generated by land use change to the different environmental and natural resources.
The combination of remote sensing and geographical information system (GIS) is an effective and powerful tool for analyzing the land cover data (Li and Gar-on yeh, 2004; Li, et al., 2005). In recent years, remote sensing and GIS has been widely used for studying the spatial and temporal transformation of land cover (Sudhira, et al., 2004; Bhatt et al., 2006;
Shlomo, et al., 2007; Huang et al., 2008; Kasimu, et al., 2008.
For the spatial development plan needs more attention on the existing features and the priority of the development plan. An sDSS is an interactive, computer-based system designed to support a user or group of users in achieving a higher effectiveness of decision making while solving a semi-structured spatial problem. It is designed to assist the spatial planner with guidance in making land use decisions (SDSS:
http://en.wikipedia.org/wiki/Spatial_Decision_Support_System).This research work is focused on issues related to the spatial decision makers for the development Plan.
1.2 Aim
The aim of the present study is to assess the use of Geographic Information system for the Spatial Decision makers through Remote sensing, Google Image, GPS and GIS for Annamalai Nagar region, Chidambaram, Cuddalore District, Tamil Nadu, India.
1.3 Research Objectives
The said aim is achieved through the following objectives,
9 to analyze the changes in Land use/land cover pattern, 9 to assess the Flood Prone zones in terms at village level,
9 to prepare the queries to locate the interest of the spatial Decision makers.
9 to prepare the Road Traffic Studies and to give remedies 9 to prepare the Road Network analysis
9 to assess the service area
9 to locate the waste disposal sites.
1.4 Study Area
The study area falls in Chidambaram Taluk, Cuddalore District of Tamil Nadu.
Annamalai Nagar has been selected for the present investigation. It lies between 11°22’18”
and 11°23’59” N latitudes, and 79°41’52” and 79°43’60” E longitudes covering an area of 6.05 km2. Annamalai University Satellite image, Google map of the study area map are shown in Fig 1.1 and 1.2.
13
1.5 Organization of the Document
Chapter I discusses, the background details of the Annamalai University, and with the Aim and Research Objectives.
Chapter II discusses the detailed thematic map generation in ERDAS and GIS thematic preparation procedure and its details.
Chapter IV discusses about the processes and Results of the present study.
Chapter V gives Research Summary of the present investigation.
Fig. 1.1. Study Area Map (Annamalai nagar - Satellite image map)
Fig.1.2. Study Area Map (Google image map)
15
CHAPTER II
THEMATIC MAP GENERATION
2.1 Study Area Map Preparation 2.1.1 Data
IRS P6 L4 MX data of July 4th 2004 spatial resolution of 5.8 meter and Google image data of 2009 from the Google source have been used for land use/land cove map preparation. Losses and gains in area from 2004 to 2009 have been calculated for each category using GIS, and error matrix has been prepared for land use change. This land use change matrix helps to understand the major changes and for taking any spatial decision.
2.1.2 Data Processing
The base map was prepared using toposheet no. 58 M/11 on 1:50,000 scale. IRS P6 L4 MX image were rectified using 1:50,000 topographic maps and then reprojected to UTM projection system. The satellite image was resampled using nearest neighbor algorithm.
This study is based on detection of changes of surface reflectance of object. So for this relative radiometric correction has been done with image regression (Jensen 1966).
Brightness value of pixels of all bands in the two satellite data were calibrated by using linear regression equation for better interpretation.
For classification, first training sites have been defined for Residential area, Crop Land, Fallow Land, Waste land, Wet Land and Waterbody. Then these classes have been classified using supervised approach of classification. Manual editing has been used to remove mixing of pixel between different classes and addition of missing pixels in the classified data.
Then the map was exported to GIS as GEOTIFF format. In GIS, the following steps were used for the preparation of thematic map.
The Arc/Catalog was used from the preparation base work like.
1. Geo Database Creation 2. Feature Data Set Preparation 3. Feature Class Preparation
In feature Class, Point, Line and Polygon feature class were prepared. The following datasets were prepared (Table 2.1).
Table 2.1 Feature Class and Feature Type Sl. No. Feature Class Feature Type
1 Boundary Polygon
2 Land Use / Land cover Polygon
3 River Polygon
4 Road Line
5 Locations Point
2.2 Road Network Map Preparation 2.2.1 Thematic Map - Roads
Road network maps were prepared from various sources (toposheet, satellite image (IRS P6 LISS-IV MX Data) and Google image). All the roads were digitized in GIS environment. Road network maps are given in Fig. 2.1 to 2.3. The maps are clearly depicts the changes in the length of the road over a period time. Using this, change detection analysis was made. The detailed map results were given in the third chapter.
2.2.2 Road Traffic Map Preparation
The field data with respect to the number of vehicles passed in a road was collected to assess the road traffic map. The data were collected in the morning time between 8 to 10 am and in the evening time between 5 to 7 pm. Data were collected from junction points and
17 Fig. 2.1 Road Network Map – Derived from
Toposheet – Annamalai Nagar
Fig. 2.2 Road Network Map – Derived from IRS P6 Satellite Image – Annamalai Nagar
Fig. 2.3 Road Network Map – Derived from Google Image – Annamalai Nagar
Fig. 2.4 Road Network Traffic Map – Derived from Google Image – Annamalai Nagar
in main roads. Their attributes were added in ArcGIS software and generated the road traffic map. The graduated symbol in symbology was used to prepare the Traffic maps (Fig. 2.4).
2.2.3 Road Widen (Buffer) – Road and Land use / land cover Overlay Map Preparation
The high traffic roads were selected and taken in to a separate feature and widening them by buffer analysis (http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm? Topic Name=Buffer_(Analysis)). 10m buffer zones were prepared to assess the area covered and the land use/Land cover area falls in the said category. Based on the land use/land cover on side 1 / and side 2, decision makers can take further action to which side they could plan for the development or two sides are better. 10m buffered zone map are given in Fig. 2.5.
2.2.4 Road Network Analysis
The methodology adopted in the present study is shows in Fig. 2.6 in the form of a flow chart.
Fig. 2.6 Road Network Analysis Methodology Flow chart PROCUREMENT OF GOOGLE IMAGE DATA
GEOREFERENCECING
ROAD NETWORK DIGITIZATION
TOPOLOGY
GEOCODING
NETWORK ANALYSIS
MODULE – 1 Network Tracing
MODULE – 2
Utility Road Network Analysis
MODULE – 3
Service Area Road Network Analysis
19 Fig. 2.5 Road 10m Buffered Map – Derived from
Google Image – Annamalai Nagar
Fig. 2.7 Waterbody Map – Derived from Toposheet – Annamalai Nagar
In the Network Analyst Window, the analysis layer is made up of three network analysis classes: Stops, Routes, and Barriers. Adding the starting stop locations at Karur Vysya Bank and Tamil Department (Annamalai University) creates a network analysis object in the Stops class. Similarly, adding a barrier at inserts a road damage location in network analysis. Running the route solver creates a new network analysis object in the Routes class.
There are eight kinds of network locations that function as inputs in ArcGIS Network Analyst (http://www.esri.com/software/arcgis/extensions/networkanalyst/usage.html): stops, barriers, facilities, incidents, origins, destinations, orders, and depots were learned in the present study.
• Stops are locations among which a least-cost route is calculated in a route analysis.
You can preset their order or the route solver can set them in a way that minimizes total cost. Furthermore, a start and end stop can be set and the solver will determine
the best order for all the other stops.
• Barriers are locations where the analysis should not traverse. Barriers can be used to represent locations where the analysis can't pass through; for instance, a blocked intersection. You can model road closures or accident sites as barriers if you want the route to avoid that point.
• Facilities are locations used in closest facility and service area analyses. In closest facility analysis, you search for the closest set of locations (facilities) from other locations (incidents). In service area analysis, the location for which the service area is being calculated is the facility.
• Incidents are used in closest facility analysis and represent the locations for which the nearest facility is sought.
• Origins are used in an origin-destination (OD) cost matrix as starting locations from where the route costs to destinations are calculated.
• Destinations are network locations that are used in an OD cost matrix analysis to generate lines. An OD cost matrix is a table of route costs from origins to destinations.
• Orders are network locations that are used in vehicle routing problem analysis to represent customers that require some kind of on-site service such as a delivery, pickup, or inspection visit.
• Depots are network locations that are used in vehicle routing problem analysis to represent starting, ending, or renewal locations for each route that is part of the analysis.
2.2.5 Service area Analysis (Network Analysis)
One simple way to evaluate accessibility is by a buffer distance around a point. For example, to find out how many customers live within a 0.5-kilometer radius of a site using a
21
simple circle? However, considering people travel by road, this method won't reflect the actual accessibility to the site. Service networks computed by ArcGIS Network Analyst can overcome this limitation by identifying the accessible streets within 0.5 kilometers of a site via the road network. Once created, we can use service networks to see what is alongside the accessible streets, for example, find competing businesses within a 2-minute drive.
2.3 Waterbody Map Preparations
Waterbody maps were prepared from various sources (toposheet, satellite image (IRS P6 LISS-IV MX Data) and Google image). All the waterbodies were digitized in GIS environment. Waterbody maps are given in Fig. 2.7 to 2.9. To anlayse the changes in the waterbody features, these maps were prepared. The changes were taken from the attributes of the tank in the GIS. Using this, the change detection analyses were carried out.
Fig. 2.8 Waterbody Map – Derived from IRS P6 Satellite Image – Annamalai Nagar
Fig. 2.9 Waterbody Map – Derived from Google Image – Annamalai Nagar
2.4 River
2.4.1 River Map Preparation
River maps were prepared from toposheet, satellite image (IRS P6 LISS-IV MX Data) and recent satellite image (Google image) (http://www.esri.com/software/arcgis/
extensions/networkanalyst/usage.html). The river features were digitized in GIS environment. The output maps are given in Fig. 2.10 to 2.12.
2.4.2 Flood Hazard Zone Map preparation
This area is usually affected by flooding in all the years. Hence, using the available data the analyses were carried out for the flood zone demarcation. Flood Hazard Zone map was prepared using river features. To assess the floods, buffer zones were created as a first step. This is the immediate flooding zones during the excess water carried by the river will be deposited in the adjoining area (Fig 2.13).
2.5 Remote Sensing 2.5.1 Image Enhancement
Satellite images (IRS P6 LISS-IV MX Data) were processed using ERDAS 9.3 image processing software for prepping the land use / land cover thematic maps. Geometric correction can make image coordinate system in accordance with maps coordinate system being used, so object position visibility can be plotted on maps. Radiometric correction can improve the quality of the image due to recorded materials influence.
Image enhancement is the process of making an image more interpretable for a particular application (Faust, 1989). Enhancement makes important features of raw, remotely sensed data more interpretable to the human eye. Enhancement techniques are
23 Fig. 2.10 River Map – Derived from Toposheet –
Annamalai Nagar
Fig. 2.11 River Map – Derived from IRS P6 Satellite Image – Annamalai Nagar
Fig. 2.12 River Map – Derived from Google Image – Annamalai Nagar
Fig. 2.13 Land Use/Land Cover Map – Derived from IRS P6 Satellite Image – Annamalai Nagar
often used instead of classification techniques for feature extraction-studying and locating areas and objects on the ground and deriving useful information from images.
The process of visually interpreting digitally enhanced imagery attempts to optimize the complementary abilities of the human mind and the computer. The mind is excellent at interpreting spatial attributes in an image and is capable of identifying obscure or subtle features (Liliesand and Keifer 1994). The land use classification adopted by the National Remote Sensing Agency, Hyderabad (NRSA, 1996), is presented in Table. 2.2. This classification was used for classifying objects in the present investigation.
Table 2.2. Land use/ Land cover classification (NRSA)
Sl.No. Level I Level II Sl.No. Level I Level II 1 Built –up land Town /City
Village
5 Water Bodies
River/Stream Canals
Lake/Reservoir/Tanks 2 Agricultural
land
Crop Land Fallow Land Plantations
6 Others Grass/Grazing Land Salt Pans
3 Forest Evergreen/Semi Evergreen
Deciduous (Moist & Dry) Scrub Forest
Forest Blanks Forest Plantation Mangrove
4 Waste land
Salt Affected Land Water Logged Land Marshy/Swampy Land Gullied/Ravenous Land Land with Scrub Land Without Scrub Sandy Area
Mining /Industrial wasteland
Barren rocky/ Stony Waste
25
2.5.2 Land use / Land cover map preparation from Satellite Image
The IRS P6 LISS-IV MX satellite data were classified applying supervised image classifications algorithms. The purpose of digital image classification was to produce thematic maps, where each pixel was assigned on the basis of spectral response to a particular theme. The methods of image classification were largely based on the principles of pattern recognition. A pattern may be defined as a meaningful regularity in the data that can be identified during the classification process, Land use / land cover maps are given in Fig. 2.13.
2.5.3 Land use / Land cover map preparation from Google Image
Land use/land cover map were prepared from Google image. All the features were identified are digitized in GIS environment. Google image based land use/land cover map is given in Fig. 2.14.
2.6 Overlay Analysis output Thematic Map preparation 2.6.1 Road over Land use/ Land cover map preparation
To assess the widening of the roads, this analysis was carried out. Annamalai Nagar Google image Road 10m Buffered map and Land use/Land cover map were taken for the overlay analysis and these maps were integrated one over the other to find out the number of combinations found and will lead to take spatial decision for widening the roads. The results show that number of combinations. It is highly helpful for the assessment of how much of land is feasible for the development and how much is loss for planning the development could be estimated. The detailed results were discussed in the results chapter.
Fig. 2.14 Land Use Land Cover Map – Derived from Google Image – Annamalai Nagar 2.6.2 River buffered zone map over village map – Flood zone hazard map preparation
To assess the flood zone hazard villages, this analysis was carried out. Annamalai Nagar Google Image River 500m Buffered map and village map boundary were taken. Then, these maps were integrated one over the other to find out the number villages affected at the time of the flooding. The village falls in the flood zone and its area coverage results are given in chapter III.
2.7 Change Detection Analysis Map Preparation
The procedure adopted in this research work forms the basis for deriving of land use/land cover in deferent year image interpretation and correlation works were made. The methodology is shown in flowchart Fig.2.15. The error matrix also used in this study to assess the accuracy assessment.
27
Fig. 2.15 Flow Chart of Change Detection Studies Methodology 2.8 GIS Based Solutions for Waste Disposals
The geology of the area was prepared using Geological Survey of India map. The map was traced, registered and digitized. Soil Depth and Soil permeability of the study area was prepared using soil survey & land use organization map. Then, these maps were integrated one over the other to find out the best combinations for waste disposal area. The Geology map was superposed over Soil Depth map the results map is designated as output map 1. This output map 1 was superposed over Soil permeability map and the result output map-2 is arrived with 5 combinations.
Maximum Likelihood
Satellite Data
IRS P6 LISS-IV MX Data
04 July 2004 Recent Google Image
Image Processing
Band Combination (FCC) Assessment
Area Extraction
Ground Truth Information
Supervised Classification
Change Detection of Land Use Land Cover Land Use/Land Cover
CHAPTER III
PROCESSES AND RESULTS
3.1 Introduction
The objective of this study forms the basis to assess the changes and to give solution to the spatial decision makers. The processes results are presented in the form of figures and tables, charts and statistical tables. In this study, GIS based road development, Roads Network analysis, Road map widening, service area assessment, Water body and its changes, land use/land cover changes, overlay analysis to find lass of land, buffer analysis to find the area of widening of roads and flood hazard villages zone demarcation in Annamalai Nagar were studied using the toposheet, satellite image and Google image of study area.
3.2 Road Network Analysis – Change Detection Analysis
Processes: The road map was digitized in ArcGIS environment from toposheet, satellite image and Google image. These maps were scanned, registered and digitized.
These maps were superposed one over the other to find out the changes over the period of study. The spatial analysis tools were used. The maps were overlaid (union) one over the other.
The road map results from multiple sources (Topo, IRS and Google Data output) were correlated with each other. The map output changes are shown in Fig.3.1 and its results are given in Table 3.1. It reveals that, huge amount of changes were noticed. The first correlation shows that from the year 1971 to 2004 road length changes were noticed with 55.71% growth. And in the second correlation results were reveals that from the satellite data to Google data output gives the changes of an account of 12.54%. This is due
29 3.3 Road Traffic Analysis
Field data were collected in all the roads in two hours spell in the morning and evening. Traffic map results are given in Table 3.2. More than 1000 vehicles passing roads is considered as highly traffic roads in the study area. This road is selected for widening the road in to two way line to locate the future development. This is executed by the process of 10m buffering on both side. The results are given in Fig. 3.2. and Table 3.2.
After doing this, to assess the land loss in various land use category, the details of the land were clipped out and it is given in Fig 3.3. and Table 3.4.
Fig. 3.1 Toposheet, Satellite Image and Google Image Road - Overlay Map
Fig. 3.2 Google Image Road – Highly Traffic Road – Two Side 10m Buffered Zone Map
Fig. 3.3 Road 10m Buffered Zone Map and Land use/Land cover Map Overlay Map Table 3.1 Change Detection Analysis Results of Roads – Toposheet, Satellite Image and
Google Image
Sl.No. Features Toposheet – Length in m
Satellite Image – Length in m
Change or New Road From Toposheet and satellite Image
2004
Google Image – Length in m
Change or New Road From satellite Image
2004 and Google Image
1 Chidambaram to TS
Pettai 1950 1952.01 Change 2.01 1952.01 -
2 O.P to Chandiramalai 1917.33 New Road 1917.33 -
3 Ayyappan Kovil to
Administrative Office 887 870.16 Change -16.84 870.16 -
4 Muthaiyanagar 50.31 New Road 50.31 -
5 Dobi Garden 125.73 New Road 125.73 -
6 Rajendran Statue to Annamalai Nagar
1522 1570.45 Change 48.45 1570.45 -
31 municipality
7 Chemistry Dept to
Therkiruppu main Road 464.62 New Road 464.62 -
8 Manroad 1501.25 New Road 1501.25 -
9 Chidambaram to
Kavarapattu 817.36 New Road 817.36 -
10 Kalikoil Street 515.72 New Road 515.72 -
11 Medical College Lab to
Music collage 433.60 New Road 433.60 -
12 Dental to Examination 248.99 New Road 248.99 -
13 Dental entrance 229.84 New Road 229.84 -
14 Kothangudi Road New Road 599.81 New Road
15 Poomakovil to
Therkirupu main Road 398.49 New Road 398.49 -
16 General Library Road to
Tamil Dept 334.00 New Road 334.00 -
17 Yoga centre to
Commerce Dept 100.69 New Road 100.69 -
18 Commerce Dept to Uma
Lodge 297.65 New Road 297.65 -
19 Administrative Office
Entrance New Road 166.78 New Road
20 NCC Office Road New Road 179.15 New Road
21 Physical Education Men
Hostel Road New Road 483.70 New Road
22 Dental To OP Gate 516.23 New Road 516.23 -
23 Rose Hostel Road 340.83 New Road 340.83 -
24 VC House to Rose 96.47 New Road 96.47 -
Hostel
25 Agricultural Dept. to
Helipad 801.88 New Road 801.88 -
26 Chandiramalai to
Sivaburi 824 693.13 Change -131 693.13 -
27 Post Office to Tamarai
Hostel - 149.08 New Road
28 Music Dept to Earth
Sciences - 126.51 New Road
29 Post Office to
Agricultural Dept. Road - 341.54 New Road
Total 5,183 14,276.74 16,323.31
Table 3.2 Road Traffic Results – in Annamalai Nagar
S.NO. Location No. of Vehicles Passing
1 Chidambaram to TS Pettai 4210
2 O.P to Chandiramalai 1100
3 Ayyappan Kovil to Administrative Office 2607
4 Muthaiyanagar 1300
5 Dobi Garden 150
6 Rajendran Statue to Annamalai Nagar municipality 5122
7 Chemistry Dept to Therkiruppu main Road 1600
8 Manroad 1210
33
9 Chidambaram to Kavarapattu 3120
10 Kalikoil Street 158
11 Medical College Lab to Music collage 1300
12 Dental to Examination 302
13 Dental entrance 325
14 Kothangudi Road 1300
15 Poomakovil to Therkirupu main Road 1650
16 General Library Road to Tamil Dept 610
17 Yoga centre to Commerce Dept 325
18 Commerce Dept to Uma Lodge 311
19 Administrative Office Entrance 10
20 NCC Office Road 500
21 Physical Education Men Hostel Road 1800
22 Dental To OP Gate 610
23 Rose Hostel Road 420
24 VC House to Rose Hostel 210
25 Agriculture Dept. to Helipad 200
26 Chandiramalai to Sivaburi 1200
27 Post Office to Tamarai Hostel 500
28 Music Dept to Earth Sciences 25
29 Post Office to Agriculture Dept. Road 1300
Table 3.3 Road 10m Buffered Zone Results – in Annamalai Nagar
S.NO. Road Area in Km2
1 Both Side 10 m Buffered Road 0.32
Table 3.4 Road 10m Buffered map and Land use/Land cover Map Overlay Result – in Annamalai Nagar
S.No. Road Area in m2
1 Buffered Road - Alluvium 4.53
2 Buffered Road - Crop Land 47.52
3 Buffered Road - Fallow Land 16.83
4 Buffered Road - Residential Area 234.23
5 Buffered Road - Waste Land 0.054
6 Buffered Road - Wet Land 13.53
3.4 Road Network Analysis- Shortest Route Assessment
Karur vysya bank to Tamil Department (Annamalai University) locations were selected to find the shortest route. The network analysis tools were operated and the
35
shortest route is found out (Fig. 3.4). To find out the other alternate route, the network analyst tool helps the decision makers to find other shortest route. It could be auctioned easily and quickly by using these aspect utility road network analyses to display the shortest route. The prepared print screen outputs are given bellow (Fig. 3.5.).
Fig. 3.4 From Karur vysya bank To Tamil Department (Annamalai University) Road Stops Created Map
Fig. 3.5 Alternate Shortest Route Map
3.5 Service area Analysis (Network Analysis)
Decision makers (sitting in statistics department circle) wants to see the accessible departments within 0.5 kilometers of a site via the road network. This could be achieved by the service area analysis tool in the network analyst. The centre point is selected, and the tool is run from the point to the surrounding 5 km radius area. The road network is found out.
The resulted service area is highlighted in Fig. 3.6.
Fig 3.6 Service Area from the Centre Point
3.6 Waterbodies - Change Detection Analysis
Processes: The waterbodies were digitized in toposheet, satellite image and Google image. Photo recognition tools were used in delineating the waterbody.
Waterbodies results are given in (toposheet, satellite image and Google image) Tables 3.5.
37
Table 3.5 GIS Output - Waterbody Results and its changes
S.NO. Location
Toposheet Waterbodies Area in km2
Satellite image Warebodies Area in km2
Google image Waterbodies
Area in km2
1 Near to Children Park 0.030 0.028 0.026 2 Near to Children Park 0.034 0.032 0.031 3 Near to Sivan Temple 0.0037 0.0037 0.0037 4 Near to Municipal Back Side 0.0020 0.0021 0.0019
3.7 Rivers – Change Detection Analysis
Processes: The River details were digitized in toposheet, satellite image and Google image. Photo recognition tools were used for the delineation of the rivers from the satellite data.
The Table 3.6 shows the changes of each river category for 1971 Toposheet, 2004 satellite image (IRS P6 LISS-IV MX Data). Rivers in 1971 occupies 3.01 Km length and it is in 2004 satellite image occupies in 1.58 km length.
Table 3.6 GIS Output - River Length Results and its changes S.NO. River Name In Toposheet -
Length in km
In Satellite image - Length in km
In Google image - Length in km
1 Uppanar River 1.29 0.49 0.49
2 Palaman River 0.88 0.41 0.41
3 Chidambaram pettai River 0.15 0.17 0.17
4 Palaman River 0.69 0.51 0.51
3.8 Data and Maps Analysis for Change Detection
Land use / land cover spatial distribution map prepared from IRS P6 L4 MX data (Fig.3.7) and Google image (Fig.3.8) are shown below. In order to get all these informations unified, it is essential to integrate these data with appropriate factor. Therefore, numerically these informations are integrated through the application of GIS. Various thematic maps are reclassified on the basis of weightage assigned, and brought into the "Raster Calculator"
function of Spatial Analysist tool for integration. A simple arithmetical model has been adopted to integrate two thematic maps. The final (change detection) map (Fig.3.9) reveals that there are 6 combinations Table 3.7.
Table 3.7 contains land use GIS results from 2004 to 2009, From this Table we can say that there is an enormous from change in land use in 5 years.
Fig. 3.7 Satellite Image – Land use/Land cover Map
Fig. 3.8 Google Image – Land use/Land cover Map
39 Fig. 3.9 Land Use/Land Cover – IRS P6 Satellite
Image and Google image Overlay Map
Fig. 3.10 Change Detection Studies Using IRS P6 LISS-IV MX and Google Image
Table 3.7. GIS Output - Land Use/Land Cover Distribution Results LANDUSE/LAND
COVER CATEGORIES
2004 (IRS P6 Image) 2009 Google Image Variation Area in
km2 Percentage Area in
km2 Percentage Area in
km2 Percentage
Alluvium 0.023 0.38 0.025 0.41 0.002 0.03
Crop Land 3.29 54.17 2.73 44.95 -0.56 -9.22
Fallow land 0.65 10.70 0.80 13.17 0.15 2.47
Residential Area 1.95 32.11 2.02 33.26 0.07 1.15
Waste Land 0 0.00 0.052 0.86 0.052 0.86
Waterbodies 0.06 0.99 0.026 0.97 0.02 -0.2
Wet Land 0.1 1.65 0.42 6.92 0.32 5.27
Total 6.073 100 6.073 100
Residential area is increased from 1.95 km2 in 2004 to 2.02 km2 in 2009. Agricultural area with fallow land also increased during 2004-2009. Wet land is also increased during
2004-2009 from 0.1 km2 to 0.42 km2 Crop land is decreased from 3.29 km2 in 2004 to 2.73 in 2009. Water bodies are also decreased from 0.06 km2 in 2004 to 0.026 km2 in 2009.
The graphical representation of land cover is show in Fig. 3.10. From the graph it is clearly visible that there is decrease in Crop land and waterbody and increase in Residential and agricultural area (inclusion of fallow land). Land use spatial distribution maps is displayed in Fig. 3.7 and 3.8 were prepared using IRS P6 L4 MX data of 2004 and Google image 2009 is 82.49% and 87.79% respectively.
3.9 2004 - 2009 Period Error Matrix
In order to detect land use/land cover changes and quantify the changes effectively, land use/land cover maps of the study area were first derived from IRS P6 satellite image and then simplified into six classes to examine the spatial extent of residential area Google image and error matrix results in Table 3.8. The classification carried out (for the integration map) produced an overall accuracy of. 82.49%. The error matrix also reveals the results of both user's and producer's accuracy. From this point of view, agricultural areas are classified as Crop land and fallow land areas to some extent.
Table 3.8. Error Matrix of the IRS P6 LISS-IV MX Data 2004 Vs Google Image 2009
Alluvium Crop
Land
Fallow
Land Residence Waste Land
Water Bodies
Wed
Land Total
User’s accuracy
%
Classified Map
Alluvium 0.025 Nill Nill Nill Nill Nill Nill 0.025 100 Crop Land Nill 2.42 0.52 0.59 0.023 Nill Nill 3.553 68.11
Fallow
Land Nill Nill 0.56 0.21 Nill Nill Nill 0.77 72.72 Residence Nill Nill Nill 1.3 Nill Nill Nill 1.3 100
Waste
Land Nill Nill Nill 0.026 Nill Nill Nill 0.026 100 Water
Bodies Nill Nill Nill Nill Nill 0.025 Nill 0.025 100 Wed Land Nill 0.28 Nill Nill 0.1 Nill Nill 0.38 73.68
Total 0.025 2.91 1.08 2.126 0.123 0.025 0 - - Producer’s
accuracy
%
100 83.16 51.85 61.15 81.30 100 100 - -
41
3.10 Floods Affected Area Demarcation - Buffered Analysis
Floods are among the most devastating natural hazards in the world, claiming the largest amount oflives and property damage (CEOS, 2003). Remotelysensed data play an integral role in reconstructing therecent history of the land surface and in predicting hazard events such as floods, subsidenceevents and other ground instabilities.
Reconstruction of past erosion, deformation, and deposition and quantification of tectonic, climatic, and biologic inputs–including human-induced changes–to the evolving landscape underpin the ability to develop a process based understanding of the Earth’s dynamic surface.
Processes: Google image based delineated river maps were used for the flood hazard zone map preparation. This map gives the recent changes in the river course. Buffer tools were used to buffer the line (River) data. Single buffer zone operation was used.
Distance was kept as 500m.
500m river buffered map reveals that 10.72 km2 area covers in buffered zone. This area is usually flood affected area in all the years in the study area. The map and its results are given in Fig. 3.11 and Table 3.9.
The flood zone map was overlaid with Village boundary map output map to assess which village falls in flood affected area. Village wise flood affected area map and its results are given in Fig. 3.12 and Table 3.10. The final flood map reveals that thirteen villages were falls in floods zone.
Table 3.9 River 500 m Buffered Result – in Annamalai Nagar and in surrounding area
S.No. River Area in Km2
1 Both Side 500 m Buffered River 10.72
Table 3.10 Village Wise Flooded Area – in Annamalai Nagar and its surrounding area S.No. Villages Area in Km2
1 Meethikudi 0.22
2 Varagoor 0.27
3 Sivapuri 1.03
4 Pettai 1.11
5 Usupoor 1.37
6 Sithalapaddi 0.67
7 Kavarapattu 0.15
8 Vasaputhur 0.04
9 Kumaramangalam 0.51
10 Thiruvakulam 3.12
11 Kothankudi (c) 1.42
12 Chidambaranathampettai 0.24 13 Chidambaram ( Non-m) 0.56
43
Fig. 3.11 River - 500m Buffered Zone Map Fig. 3.12 Village Wise Flood Affected Area Map
3.11 Image Enhancements Study – Vegetation Class Analysis
Image enhancement is the process of making an image more interpretable for a particular application (Faust, 1989). Enhancement makes an important feature of raw, remotely sensed data more interpretable to the human eye. Enhancement techniques are often used, instead of classification techniques for feature extraction—studying and locating areas and objects on the ground and deriving useful information from images and to extract fine change.
3.11.1 Low-Pass Filtering
The simplest example of this relationship is the low-pass kernel. The name, low-pass kernel, is derived from a filter that would pass low frequencies and block (filter out) high frequencies. In practice, this is easily achieved in the spatial domain by the M = N = 3/3
kernel. Low-pass kernel increases, the calculation becomes more time consuming.
Depending on the size of the input image and the size of the kernel, it can be faster to generate a low-pass image via Fourier processing. Low pass 3/3 kernel results image reveals that vegetation was brought highlighted as Violet color (Fig.3.13).
3.11.2 High-Pass Filtering
Just as images can be smoothed (blurred) by attenuating the high frequency components of an image using low-pass filters, images can be sharpened and edge- enhanced by attenuating the low frequency components using high-pass filters. In the Fourier domain, the high-pass operation is implemented by attenuating the pixels frequencies that satisfy:
u2 v2 + D20
High pass 3/3 kernel result image reveals that, vegetation is suppressed and buildings are highlighted in this analysis (Fig.3.14).
3.12 Spatial Queries
Query builder was generated based on precise query expressions which will provide the decision makers necessary information by clicking only few buttons. These query expressions can be grouped into several classes such as simple query expression, compound query expression, etc. In simple query expression, only one mathematical (=, +, -
…) or relational operator (<, >, <=) is used whereas in compound query expression, logical (“AND”,”OR”.) operators are used along with mathematical and relational operator. In this query builder maps prepared from GIS environment.
45
If the Decision maker wants to see the particular Department or Faculty, It could be auctioned easily and quickly by using these aspect select by Attribute to display the department. The prepared print screen outputs are given bellow (Figs 3.15 to 3.24).
1. Faculty of Arts 2. Faculty of Sciences 3. Faculty of Engineering
4. Department of Physical Science 5. Administrative Office Building 6. Faculty of Medical Science
7. Annamalai University OP Hospital 8. Old OP Hospital
9. Ayyappan Temple 10. Boys Hostel
Fig. 3.13 Low Pass Filtering Output Map
Fig. 3.14 High Pass Filtering Output Map
47
Fig.3.15 Spatial Query for the Decision Makers through Defining Query for Faculty of Arts – Annamalai University
Fig.3.16 Spatial Query for the Decision Makers through Defining Query for Faculty of Science – Annamalai University
Fig.3.17 Spatial Query for the Decision Makers Through Defining Query for Faculty of Engineering – Annamalai University
Fig.3.18 Spatial Query for the Decision Makers Through Defining Query for Department of Physical Sciences – Annamalai University
49
Fig.3.19 Spatial Query for the Decision Makers Through Defining Query for Administrative Office – Annamalai University
Fig.3.20 Spatial Query for the Decision Makers Through Defining Query for Faculty of Medical Science – Annamalai University
Fig.3.21 Spatial Query for the Decision Makers through Defining Query for Annamalai University OP Hospital – Annamalai University
Fig.3.22 Spatial Query for the Decision Makers through Defining Query for Annamalai University Old OP Hospital – Annamalai University
51
Fig.3.23 Spatial Query for the Decision Makers through Defining Query for Ayyappan Temple – Annamalai University
Fig.3.24 Spatial Query for the Decision Makers through Defining Query for Boys Hostel – Annamalai University
3.13 GIS Based Solutions for Waste Disposals 3.13.1 Introduction
The phenomenal population explosion has led competitive and unplanned exploitation of the planet earth which has resulted irreparable damage to the environment.
Such improper exploitation of natural resources and the interaction of human beings with earth’s ecosystems has not only depleted the natural resources but also triggered off the natural morphodynamic processes of the earth which in turn are causing natural disasters and chains of environmental problems such as landslides, land subsidence, soil erosion, reservoir siltation, flooding, water logging, coastal erosion, etc. On the other hand, domestic, industrial and other wastes, whether these are of low or medium level wastes, are causing environmental pollution and have become a perennial problem for the mankind. However, while the human-induced environmental problems warrant detailed studies, the environmental pollution due to waste disposals can be overcome by selecting suitable sites through careful understanding of the lithospheric and hydrospheric conditions of the planet earth. The art of remote sensing is an excellent tool in mapping such lithospheric and hydrospheric parameters and the GIS is a proven tool in storing, retrieving, analysing and amalgamating all such parameters to select suitable sites for such waste disposals. The present work brings out a certain newer package of information on how suitable sites can be identified for disposing wastes using remote sensing and GIS technologies.
3.13.2 Geology
Geologically the region comprises of alluvial deposits of early to middle Pleistocene age. The study area is mainly underlined by Clays with Limestone Bands, Lenses (50.41%), Strandlines Deposit (6.94%) and Palaeo Channel Deposit (42.64%) (Fig.3.25). The major part of the area is covered by clay with Limestone bands, Lenses and Palaeo Channel
53
Deposit. The spatial distribution results of the geological units are given in the Table 3.11.
Clay with limestone bands, lenses type and its associated combinations are usually acted as a favorable zone for waste disposals. Because this area will not allow the water to penetrate down to touch the water table.
Table 3.11 Geology – GIS Spatial Distribution Results
Sl.No. Rock Types Area in Km2 Per/cent
1 Clays with Limestone Bands, Lenses 3.05 50.41
2 Strandlines Deposit 0.42 6.94
3 Palaeo Channel Deposit 2.58 42.64
3.13.3 Soil Depth
Effective soil depth refers to the depth of soil. The depth of soil is restricted by parent material and hard pans, water table, erosion, salinity, alkalinity etc., Eroded soil have poor depth. Plant growth is generally influenced by the depth of the soil. Root penetrations, type of cultivar or plant to be grown are directly linked with solum depth. The spatial distribution map (Fig. 3.26) and results are given in the table 3.12. Deep soil depth is usually acted as a favorable zone for waste disposals sites.
Table 3.12. Soil Depth – GIS Spatial Distribution Results Sl.No. Soil Depth Area in Km2 Per/cent
1 Deep Soil Depth 0.68 11.24
2 Very Deep Soil Depth 5.37 88.76
3.13.4 Soil Permeability
This refers to the rate of intake of water through soil column or the amount of water that would move downwards. The degree of permeability plays an important role in the natural drainage of that area. Soils with moderately slow/ slow permeability are likely to cause drainage problems as the permeability decreases with increase in fine texture or clay content. The study area is mainly underlined by moderately slow type. The spatial distribution map (Fig.3.27) and its results are given in the table 3.13. Moderately slow types of soil permeability are usually acted as a favorable zone of waste disposals.
Table.3.13. Soil Permeability – GIS Spatial Distribution Results Sl.No. Soil Permeability Area in Km2 Per/cent
1 Moderately Slow 6.05 100
3.13.5 The New Concepts
The sites which attain the credentials for waste disposals are normally the
Geology
o Clays with Limestone Bands, Lenses – Weightage Index – 3 o Strandlines Deposit – Weightage Index – 2
o Palaeo Channel Deposit – Weightage Index – 1
Soil Depth
o Deep Soil Depth – Weightage Index – 2 o Very Deep Soil Depth – Weightage Index – 1
Soil Permeability
o Moderately Slow – Weightage Index – 2
55 3.13.6 GIS Analysis
Keeping this concept in mind, a model study was conducted in Annamalai Nagar Town Ship to demonstrate the concept of selecting suitable sites for the disposal industrial and domestic wastes. In the said study, the Geology (Fig. 3.25), Soil Depth (Fig.3.26) and Soil Permeability (Fig.3.27) provides certain clues for the preparation of waste disposals site selection map. In order to get all these information unified, it is essential to integrate these data with appropriate factor. Therefore, numerically this information’s are integrated through the application of GIS. Various thematic maps are reclassified on the basis of weightage assigned (Table 3.14).
Table.3.14. Various Data Layers and Waste Disposals Weighting–Rating System Adopted in this Study
Sl.No. Data Layers Classes Weighting Rating
1 Geology
Clays with Limestone Bands,
Lenses 3 3
Strandlines Deposit 2 2
Paleo Channel Deposit 1 1
2 Soil Depth
Deep Soil Depth 2 2
Very Deep Soil Depth 1 1
3 Soil Permeability Moderately Slow 2 2
The Geology map was superposed over Soil depth map, the output map 1 is designated as Geology and soil depth integration map (Fig. 3.28) and its results are given in the table 3.15. The results show that number of combinations. It is highly helpful in assessing the best site selection of waste disposals area. There are five combinations were observed in the results, such as Clays with Limestone Bands, Lenses-Deep Soil Depth, Clays with Limestone Bands, Lenses-Very Deep Soil Depth, Strandlines Deposit-Deep Soil
Depth, Palaeo Channel Deposit- Deep Soil Depth and Palaeo Channel Deposit-Very Deep Soil Depth. Clays with Limestone Bands, Lenses-Deep Soil Depth combination covers an area in 0.68 Km2. These combinations are the highly favourable for locating waste disposals area.
This output map 1 was superposed over Soil Permeability map and the result output map-2 is shown in Fig. 3.29. The final output map-2 shows that there are 5 combinations (Table 3.16). Clays with Limestone Bands, Lenses-Deep Soil Depth- Moderately Slow combination covers an area in 0.68 Km2, it is also verified in the field. This combination is highly recommended for the disposal industrial and domestic wastes.
Fig.3.25 Geology Map Fig.3.26 Soil Depth Map
57
Fig.3.27 Soil Permeability Map
Table 3.15. Geology with Soil Depth integrated map – GIS Spatial Distribution Results Sl.No. Geology with Soil Depth Area in km2 Per/cent
1 Clays with limestone Bands, Lenses -
Very Deep Soil Depth 2.37 39.24
2 Clays with limestone Bands, Lenses -
Deep Soil Depth 0.68 11.18
3 Palaeo Channel Deposit - Deep Soil
Depth 2.58 42.61
4 Palaeo Channel Deposit - Very Deep
Soil Depth 0.0007 0.01
5 Strandlines Deposit - Deep Soil Depth 0.42 6.89
Fig. 3.28 Geology and Soil Depth Integration Map
Fig. 3.29 Waste Disposals Area Map
Table 3.16. Site Section for Waste Disposal Area – GIS Spatial Distribution Results Sl.No. Geology with Soil Depth Area in km2 Per/cent
1
Clays with limestone Bands, Lenses - Very Deep Soil Depth - Moderately Slow
2.37 39.24
2 Clays with limestone Bands, Lenses -
Deep Soil Depth - Moderately Slow 0.68 11.18
3 Palaeo Channel Deposit - Deep Soil
Depth - Moderately Slow 2.58 42.61
4 Palaeo Channel Deposit - Very Deep
Soil Depth - Moderately Slow 0.0007 0.01
5 Strandlines Deposit - Deep Soil Depth -
Moderately Slow 0.42 6.89
59
CHAPTER IV
RESEARCH SUMMARY
4.1 Achievements of the Research
This research work demonstrates the ability of GIS and Remote Sensing in capturing and analyzing spatial-temporal data for the spatial decision makers. Attempt was made to capture as accurate as possible for land use / land cover classes as they change through IRS P6 LISS-IV MX data 2004 and Google Image data.
Spatial Decision makers need road assessment study with respect to the movement of vehicles. This has been achieved in the present study. This could be achieved by creating a buffer region around the plan area to such extent that the peripheral influence on the study area is minimized. Apart from this, finding the shortest path or alternate path and its details is achieved in this study. Service area assessment from the junction point is assessed in this study.
Changes in the land use / land cover over the period of time in an accurate way are achieved through GIS. The efficiency of the GIS is proved in this type of analysis.
Flood zone demarcation needs more parameters. Using multiple parameters, this could be achieved in GIS. A buffer in river sides based GIS model will be an appropriate tool in such an environment.
Many times, the spatial decision makers for the planning and development activities will depend on the spatial distribution of the resources. This was achieved in the present study to locate various faculties through select by attribute method.
Finally the waste disposal site locations are predicted in the study.
4.2 Conclusion
4.2.1 Change Detection Study
The study has indicated the potential use of remote sensing data in studying various land use/ land cover pattern change. GIS techniques integrated in this study has proved beyond doubt its capabilities of spatial analysis. In this study, IRS P6 LISS-IV MX image and Google Image were used satisfactorily for the identification of land use land cover details.
It’s observed that the area under the said category changed during 2004 - 2009 remarkably.
Decrease in vegetation has been as a result of enhancement of building (Residence) activities in the study area. In conclusion for detecting changes in areas based on a subject vegetation area change into residential area, over a period of years both spatial and in quantitative way, integrating remote sensing data and GIS techniques will be useful.
4.2.2 Road Network Analysis
The number of vehicle passing data interpretation during the four hours data reveals that the highly traffic road were noticed is bus stand to Rajendran statue via Annamalai Nagar. It covers 9896 m length in the study area.
Finding the shortest route between destinations is achieved in the study. Or to locate the alternate route is also analyzed.
Service area assessment with respect to the Paris corner and the user locations is assessed.
4.2.3 GIS Overlay Analysis
Annamalai Nagar Road 10m Buffered map and Drainage 500m Buffered thematic maps were prepared from GIS environment. Annamalai Nagar Road buffered map was superposed over Land use/Land cover map, the output map-1 is designated as SDSS Map for 500m Buffered Road and Land Use/ Land Cover Overlay Map. The results show that
